Overview

Dataset statistics

Number of variables26
Number of observations7012
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.8 MiB
Average record size in memory1.1 KiB

Variable types

Numeric6
Text15
Categorical5

Alerts

SCHOOLYEAR has constant value "2020-2021"Constant
CBSATYPE is highly overall correlated with LOCALEHigh correlation
LAT is highly overall correlated with YHigh correlation
LOCALE is highly overall correlated with CBSATYPEHigh correlation
LON is highly overall correlated with XHigh correlation
NECTA is highly overall correlated with NMNECTAHigh correlation
NMNECTA is highly overall correlated with NECTAHigh correlation
OBJECTID is highly overall correlated with UNITIDHigh correlation
UNITID is highly overall correlated with OBJECTIDHigh correlation
X is highly overall correlated with LONHigh correlation
Y is highly overall correlated with LATHigh correlation
CBSATYPE is highly imbalanced (54.2%)Imbalance
NECTA is highly imbalanced (91.2%)Imbalance
NMNECTA is highly imbalanced (91.2%)Imbalance
OBJECTID is uniformly distributedUniform
OBJECTID has unique valuesUnique
UNITID has unique valuesUnique

Reproduction

Analysis started2025-12-08 06:35:07.498087
Analysis finished2025-12-08 06:35:09.401908
Duration1.9 second
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

X
Real number (ℝ)

High correlation 

Distinct6867
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-90.358006
Minimum-170.74277
Maximum171.37813
Zeros0
Zeros (%)0.0%
Negative7005
Negative (%)99.9%
Memory size54.9 KiB
2025-12-07T22:35:09.432526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-170.74277
5-th percentile-121.5184
Q1-97.458857
median-86.138814
Q3-78.823886
95-th percentile-71.437511
Maximum171.37813
Range342.1209
Interquartile range (IQR)18.634971

Descriptive statistics

Standard deviation17.887259
Coefficient of variation (CV)-0.19795987
Kurtosis31.890932
Mean-90.358006
Median Absolute Deviation (MAD)9.4222525
Skewness1.6851804
Sum-633590.34
Variance319.95403
MonotonicityNot monotonic
2025-12-07T22:35:09.476853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-83.9086844
 
0.1%
-80.3151894
 
0.1%
-112.1046013
 
< 0.1%
-117.1920733
 
< 0.1%
-92.1165043
 
< 0.1%
-83.4309583
 
< 0.1%
-81.1965033
 
< 0.1%
-96.6821663
 
< 0.1%
-84.1708322
 
< 0.1%
-87.626712
 
< 0.1%
Other values (6857)6982
99.6%
ValueCountFrequency (%)
-170.7427741
< 0.1%
-159.3958831
< 0.1%
-158.081611
< 0.1%
-158.0564031
< 0.1%
-157.9837711
< 0.1%
-157.9265861
< 0.1%
-157.8883791
< 0.1%
-157.8738152
< 0.1%
-157.871
< 0.1%
-157.8691721
< 0.1%
ValueCountFrequency (%)
171.3781291
< 0.1%
158.1581891
< 0.1%
145.7217331
< 0.1%
144.8358211
< 0.1%
144.8089441
< 0.1%
144.8039781
< 0.1%
134.4737441
< 0.1%
-64.798561
< 0.1%
-64.9728661
< 0.1%
-65.6480521
< 0.1%

Y
Real number (ℝ)

High correlation 

Distinct6867
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.144172
Minimum-14.322636
Maximum71.324702
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size54.9 KiB
2025-12-07T22:35:09.516862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-14.322636
5-th percentile26.241219
Q133.818447
median38.546171
Q341.203424
95-th percentile44.900363
Maximum71.324702
Range85.647338
Interquartile range (IQR)7.3849768

Descriptive statistics

Standard deviation5.9794824
Coefficient of variation (CV)0.16098037
Kurtosis2.5011721
Mean37.144172
Median Absolute Deviation (MAD)3.559973
Skewness-0.95493157
Sum260454.93
Variance35.75421
MonotonicityNot monotonic
2025-12-07T22:35:09.554287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.803214
 
0.1%
26.0058954
 
0.1%
33.5657583
 
< 0.1%
32.7708163
 
< 0.1%
37.7624733
 
< 0.1%
42.4192793
 
< 0.1%
28.5732753
 
< 0.1%
32.7259553
 
< 0.1%
33.9832062
 
< 0.1%
41.8854522
 
< 0.1%
Other values (6857)6982
99.6%
ValueCountFrequency (%)
-14.3226361
< 0.1%
6.9097591
< 0.1%
7.1026881
< 0.1%
7.3431771
< 0.1%
13.4330471
< 0.1%
13.4406491
< 0.1%
13.4650461
< 0.1%
15.1522471
< 0.1%
17.7161151
< 0.1%
17.966351
< 0.1%
ValueCountFrequency (%)
71.3247021
< 0.1%
64.857561
< 0.1%
64.8575161
< 0.1%
61.5985531
< 0.1%
61.1909681
< 0.1%
61.1901631
< 0.1%
61.1814431
< 0.1%
60.4904591
< 0.1%
60.1106261
< 0.1%
58.3848461
< 0.1%

OBJECTID
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct7012
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3506.5
Minimum1
Maximum7012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.9 KiB
2025-12-07T22:35:09.594032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile351.55
Q11753.75
median3506.5
Q35259.25
95-th percentile6661.45
Maximum7012
Range7011
Interquartile range (IQR)3505.5

Descriptive statistics

Standard deviation2024.3344
Coefficient of variation (CV)0.5773091
Kurtosis-1.2
Mean3506.5
Median Absolute Deviation (MAD)1753
Skewness0
Sum24587578
Variance4097929.7
MonotonicityStrictly increasing
2025-12-07T22:35:09.635719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
46721
 
< 0.1%
46831
 
< 0.1%
46821
 
< 0.1%
46811
 
< 0.1%
46801
 
< 0.1%
46791
 
< 0.1%
46781
 
< 0.1%
46771
 
< 0.1%
46761
 
< 0.1%
Other values (7002)7002
99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
70121
< 0.1%
70111
< 0.1%
70101
< 0.1%
70091
< 0.1%
70081
< 0.1%
70071
< 0.1%
70061
< 0.1%
70051
< 0.1%
70041
< 0.1%
70031
< 0.1%

UNITID
Real number (ℝ)

High correlation  Unique 

Distinct7012
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2513257.4
Minimum100654
Maximum49576723
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.9 KiB
2025-12-07T22:35:09.675884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum100654
5-th percentile117710.95
Q1175401.25
median232225.5
Q3458147
95-th percentile17718855
Maximum49576723
Range49476069
Interquartile range (IQR)282745.75

Descriptive statistics

Standard deviation8455105.3
Coefficient of variation (CV)3.3642019
Kurtosis17.775008
Mean2513257.4
Median Absolute Deviation (MAD)110933.5
Skewness4.2259476
Sum1.7622961 × 1010
Variance7.1488805 × 1013
MonotonicityNot monotonic
2025-12-07T22:35:09.715086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1006541
 
< 0.1%
4148691
 
< 0.1%
4170621
 
< 0.1%
4170081
 
< 0.1%
4168011
 
< 0.1%
4164581
 
< 0.1%
4159871
 
< 0.1%
4150571
 
< 0.1%
4150391
 
< 0.1%
4149661
 
< 0.1%
Other values (7002)7002
99.9%
ValueCountFrequency (%)
1006541
< 0.1%
1006631
< 0.1%
1006901
< 0.1%
1007061
< 0.1%
1007241
< 0.1%
1007331
< 0.1%
1007511
< 0.1%
1007601
< 0.1%
1008121
< 0.1%
1008301
< 0.1%
ValueCountFrequency (%)
495767231
< 0.1%
495767221
< 0.1%
495767211
< 0.1%
495767201
< 0.1%
495767191
< 0.1%
495767181
< 0.1%
495767171
< 0.1%
495767161
< 0.1%
495767151
< 0.1%
495767141
< 0.1%

NAME
Text

Distinct6819
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Memory size545.5 KiB
2025-12-07T22:35:09.818544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length93
Median length68
Mean length30.647747
Min length3

Characters and Unicode

Total characters214902
Distinct characters72
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6664 ?
Unique (%)95.0%

Sample

1st rowAlabama A & M University
2nd rowUniversity of Alabama at Birmingham
3rd rowAmridge University
4th rowUniversity of Alabama in Huntsville
5th rowAlabama State University
ValueCountFrequency (%)
college2406
 
8.8%
of1760
 
6.4%
university1406
 
5.1%
school603
 
2.2%
community580
 
2.1%
542
 
2.0%
institute528
 
1.9%
state440
 
1.6%
beauty437
 
1.6%
technical388
 
1.4%
Other values (5217)18302
66.8%
2025-12-07T22:35:09.970888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e21907
 
10.2%
20391
 
9.5%
o15050
 
7.0%
i14170
 
6.6%
l13257
 
6.2%
a13225
 
6.2%
t13098
 
6.1%
n13010
 
6.1%
r10092
 
4.7%
s8691
 
4.0%
Other values (62)72011
33.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)214902
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e21907
 
10.2%
20391
 
9.5%
o15050
 
7.0%
i14170
 
6.6%
l13257
 
6.2%
a13225
 
6.2%
t13098
 
6.1%
n13010
 
6.1%
r10092
 
4.7%
s8691
 
4.0%
Other values (62)72011
33.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)214902
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e21907
 
10.2%
20391
 
9.5%
o15050
 
7.0%
i14170
 
6.6%
l13257
 
6.2%
a13225
 
6.2%
t13098
 
6.1%
n13010
 
6.1%
r10092
 
4.7%
s8691
 
4.0%
Other values (62)72011
33.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)214902
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e21907
 
10.2%
20391
 
9.5%
o15050
 
7.0%
i14170
 
6.6%
l13257
 
6.2%
a13225
 
6.2%
t13098
 
6.1%
n13010
 
6.1%
r10092
 
4.7%
s8691
 
4.0%
Other values (62)72011
33.5%

STREET
Text

Distinct6851
Distinct (%)97.7%
Missing1
Missing (%)< 0.1%
Memory size474.1 KiB
2025-12-07T22:35:10.077668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length73
Median length60
Mean length20.226359
Min length1

Characters and Unicode

Total characters141807
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6718 ?
Unique (%)95.8%

Sample

1st row4900 Meridian Street
2nd rowAdministration Bldg Suite 1070
3rd row1200 Taylor Rd
4th row301 Sparkman Dr
5th row915 S Jackson Street
ValueCountFrequency (%)
st832
 
3.1%
ave787
 
2.9%
street771
 
2.8%
rd602
 
2.2%
road563
 
2.1%
avenue554
 
2.0%
drive511
 
1.9%
suite510
 
1.9%
blvd433
 
1.6%
w387
 
1.4%
Other values (6604)21268
78.1%
2025-12-07T22:35:10.227265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20327
 
14.3%
e11026
 
7.8%
t7450
 
5.3%
06629
 
4.7%
r6195
 
4.4%
a6056
 
4.3%
15601
 
3.9%
o5069
 
3.6%
i4822
 
3.4%
n4772
 
3.4%
Other values (63)63860
45.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)141807
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
20327
 
14.3%
e11026
 
7.8%
t7450
 
5.3%
06629
 
4.7%
r6195
 
4.4%
a6056
 
4.3%
15601
 
3.9%
o5069
 
3.6%
i4822
 
3.4%
n4772
 
3.4%
Other values (63)63860
45.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)141807
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
20327
 
14.3%
e11026
 
7.8%
t7450
 
5.3%
06629
 
4.7%
r6195
 
4.4%
a6056
 
4.3%
15601
 
3.9%
o5069
 
3.6%
i4822
 
3.4%
n4772
 
3.4%
Other values (63)63860
45.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)141807
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
20327
 
14.3%
e11026
 
7.8%
t7450
 
5.3%
06629
 
4.7%
r6195
 
4.4%
a6056
 
4.3%
15601
 
3.9%
o5069
 
3.6%
i4822
 
3.4%
n4772
 
3.4%
Other values (63)63860
45.0%

CITY
Text

Distinct2466
Distinct (%)35.2%
Missing0
Missing (%)0.0%
Memory size395.9 KiB
2025-12-07T22:35:10.336395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length24
Median length20
Mean length8.7979179
Min length3

Characters and Unicode

Total characters61691
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1411 ?
Unique (%)20.1%

Sample

1st rowNormal
2nd rowBirmingham
3rd rowMontgomery
4th rowHuntsville
5th rowMontgomery
ValueCountFrequency (%)
san190
 
2.1%
city161
 
1.8%
new152
 
1.7%
york97
 
1.1%
chicago80
 
0.9%
fort71
 
0.8%
houston70
 
0.8%
beach68
 
0.8%
saint62
 
0.7%
park61
 
0.7%
Other values (2302)7933
88.7%
2025-12-07T22:35:10.483220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a5670
 
9.2%
e5484
 
8.9%
o4833
 
7.8%
n4699
 
7.6%
l3992
 
6.5%
i3851
 
6.2%
r3654
 
5.9%
t3157
 
5.1%
s2766
 
4.5%
1940
 
3.1%
Other values (46)21645
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)61691
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a5670
 
9.2%
e5484
 
8.9%
o4833
 
7.8%
n4699
 
7.6%
l3992
 
6.5%
i3851
 
6.2%
r3654
 
5.9%
t3157
 
5.1%
s2766
 
4.5%
1940
 
3.1%
Other values (46)21645
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)61691
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a5670
 
9.2%
e5484
 
8.9%
o4833
 
7.8%
n4699
 
7.6%
l3992
 
6.5%
i3851
 
6.2%
r3654
 
5.9%
t3157
 
5.1%
s2766
 
4.5%
1940
 
3.1%
Other values (46)21645
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)61691
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a5670
 
9.2%
e5484
 
8.9%
o4833
 
7.8%
n4699
 
7.6%
l3992
 
6.5%
i3851
 
6.2%
r3654
 
5.9%
t3157
 
5.1%
s2766
 
4.5%
1940
 
3.1%
Other values (46)21645
35.1%

STATE
Text

Distinct59
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size349.4 KiB
2025-12-07T22:35:10.546727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters14024
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowAL
2nd rowAL
3rd rowAL
4th rowAL
5th rowAL
ValueCountFrequency (%)
ca752
 
10.7%
ny463
 
6.6%
tx453
 
6.5%
fl420
 
6.0%
pa378
 
5.4%
oh298
 
4.2%
il270
 
3.9%
mi197
 
2.8%
ga183
 
2.6%
nc181
 
2.6%
Other values (49)3417
48.7%
2025-12-07T22:35:10.644895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A2281
16.3%
N1436
 
10.2%
C1245
 
8.9%
M910
 
6.5%
L903
 
6.4%
I873
 
6.2%
T827
 
5.9%
O756
 
5.4%
Y564
 
4.0%
P555
 
4.0%
Other values (14)3674
26.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)14024
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A2281
16.3%
N1436
 
10.2%
C1245
 
8.9%
M910
 
6.5%
L903
 
6.4%
I873
 
6.2%
T827
 
5.9%
O756
 
5.4%
Y564
 
4.0%
P555
 
4.0%
Other values (14)3674
26.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14024
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A2281
16.3%
N1436
 
10.2%
C1245
 
8.9%
M910
 
6.5%
L903
 
6.4%
I873
 
6.2%
T827
 
5.9%
O756
 
5.4%
Y564
 
4.0%
P555
 
4.0%
Other values (14)3674
26.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14024
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A2281
16.3%
N1436
 
10.2%
C1245
 
8.9%
M910
 
6.5%
L903
 
6.4%
I873
 
6.2%
T827
 
5.9%
O756
 
5.4%
Y564
 
4.0%
P555
 
4.0%
Other values (14)3674
26.2%

ZIP
Text

Distinct6207
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Memory size387.2 KiB
2025-12-07T22:35:10.751119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length9
Mean length7.5215345
Min length5

Characters and Unicode

Total characters52741
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5614 ?
Unique (%)80.1%

Sample

1st row35762
2nd row35294-0110
3rd row36117-3553
4th row35899
5th row36104-0271
ValueCountFrequency (%)
009619
 
0.1%
926266
 
0.1%
234626
 
0.1%
782295
 
0.1%
00674-00005
 
0.1%
900105
 
0.1%
841075
 
0.1%
071025
 
0.1%
200055
 
0.1%
453875
 
0.1%
Other values (6197)6956
99.2%
2025-12-07T22:35:10.886868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
08425
16.0%
16564
12.4%
25379
10.2%
35013
9.5%
44406
8.4%
94380
8.3%
54046
7.7%
73957
7.5%
63845
7.3%
83473
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)52741
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
08425
16.0%
16564
12.4%
25379
10.2%
35013
9.5%
44406
8.4%
94380
8.3%
54046
7.7%
73957
7.5%
63845
7.3%
83473
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)52741
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
08425
16.0%
16564
12.4%
25379
10.2%
35013
9.5%
44406
8.4%
94380
8.3%
54046
7.7%
73957
7.5%
63845
7.3%
83473
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)52741
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
08425
16.0%
16564
12.4%
25379
10.2%
35013
9.5%
44406
8.4%
94380
8.3%
54046
7.7%
73957
7.5%
63845
7.3%
83473
6.6%

STFIP
Text

Distinct57
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size349.4 KiB
2025-12-07T22:35:10.950887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.9995722
Min length1

Characters and Unicode

Total characters14021
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row01
2nd row01
3rd row01
4th row01
5th row01
ValueCountFrequency (%)
06752
 
10.7%
36463
 
6.6%
48453
 
6.5%
12420
 
6.0%
42378
 
5.4%
39298
 
4.2%
17270
 
3.9%
26197
 
2.8%
13183
 
2.6%
37181
 
2.6%
Other values (47)3417
48.7%
2025-12-07T22:35:11.048821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22264
16.1%
41899
13.5%
31719
12.3%
11709
12.2%
01530
10.9%
61500
10.7%
51006
7.2%
7909
6.5%
8786
 
5.6%
9696
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)14021
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
22264
16.1%
41899
13.5%
31719
12.3%
11709
12.2%
01530
10.9%
61500
10.7%
51006
7.2%
7909
6.5%
8786
 
5.6%
9696
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14021
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
22264
16.1%
41899
13.5%
31719
12.3%
11709
12.2%
01530
10.9%
61500
10.7%
51006
7.2%
7909
6.5%
8786
 
5.6%
9696
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14021
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
22264
16.1%
41899
13.5%
31719
12.3%
11709
12.2%
01530
10.9%
61500
10.7%
51006
7.2%
7909
6.5%
8786
 
5.6%
9696
 
5.0%

CNTY
Text

Distinct1503
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Memory size369.9 KiB
2025-12-07T22:35:11.167497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.9982886
Min length1

Characters and Unicode

Total characters35048
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique660 ?
Unique (%)9.4%

Sample

1st row01089
2nd row01073
3rd row01101
4th row01089
5th row01101
ValueCountFrequency (%)
06037212
 
3.0%
17031118
 
1.7%
3606188
 
1.3%
0401384
 
1.2%
1208681
 
1.2%
4820175
 
1.1%
0607374
 
1.1%
0605968
 
1.0%
4811368
 
1.0%
3604752
 
0.7%
Other values (1493)6092
86.9%
2025-12-07T22:35:11.325830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
07812
22.3%
15932
16.9%
34158
11.9%
23174
9.1%
72780
 
7.9%
52759
 
7.9%
42493
 
7.1%
92397
 
6.8%
62198
 
6.3%
81342
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)35048
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07812
22.3%
15932
16.9%
34158
11.9%
23174
9.1%
72780
 
7.9%
52759
 
7.9%
42493
 
7.1%
92397
 
6.8%
62198
 
6.3%
81342
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)35048
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07812
22.3%
15932
16.9%
34158
11.9%
23174
9.1%
72780
 
7.9%
52759
 
7.9%
42493
 
7.1%
92397
 
6.8%
62198
 
6.3%
81342
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)35048
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07812
22.3%
15932
16.9%
34158
11.9%
23174
9.1%
72780
 
7.9%
52759
 
7.9%
42493
 
7.1%
92397
 
6.8%
62198
 
6.3%
81342
 
3.8%

NMCNTY
Text

Distinct1073
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Memory size437.0 KiB
2025-12-07T22:35:11.428612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length28
Median length22
Mean length14.531945
Min length1

Characters and Unicode

Total characters101898
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique372 ?
Unique (%)5.3%

Sample

1st rowMadison County
2nd rowJefferson County
3rd rowMontgomery County
4th rowMadison County
5th rowMontgomery County
ValueCountFrequency (%)
county6554
43.6%
los213
 
1.4%
angeles212
 
1.4%
san200
 
1.3%
municipio175
 
1.2%
new137
 
0.9%
parish123
 
0.8%
cook118
 
0.8%
orange116
 
0.8%
city113
 
0.8%
Other values (1096)7088
47.1%
2025-12-07T22:35:11.564917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n10886
10.7%
o10707
 
10.5%
t8477
 
8.3%
u8046
 
7.9%
8037
 
7.9%
C7289
 
7.2%
y7232
 
7.1%
a5809
 
5.7%
e5097
 
5.0%
i3537
 
3.5%
Other values (49)26781
26.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)101898
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n10886
10.7%
o10707
 
10.5%
t8477
 
8.3%
u8046
 
7.9%
8037
 
7.9%
C7289
 
7.2%
y7232
 
7.1%
a5809
 
5.7%
e5097
 
5.0%
i3537
 
3.5%
Other values (49)26781
26.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)101898
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n10886
10.7%
o10707
 
10.5%
t8477
 
8.3%
u8046
 
7.9%
8037
 
7.9%
C7289
 
7.2%
y7232
 
7.1%
a5809
 
5.7%
e5097
 
5.0%
i3537
 
3.5%
Other values (49)26781
26.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)101898
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n10886
10.7%
o10707
 
10.5%
t8477
 
8.3%
u8046
 
7.9%
8037
 
7.9%
C7289
 
7.2%
y7232
 
7.1%
a5809
 
5.7%
e5097
 
5.0%
i3537
 
3.5%
Other values (49)26781
26.3%

LOCALE
Categorical

High correlation 

Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size349.4 KiB
21
1737 
11
1640 
13
916 
12
840 
32
457 
Other values (8)
1422 

Length

Max length2
Median length2
Mean length1.9995722
Min length1

Characters and Unicode

Total characters14021
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12
2nd row12
3rd row12
4th row12
5th row12

Common Values

ValueCountFrequency (%)
211737
24.8%
111640
23.4%
13916
13.1%
12840
12.0%
32457
 
6.5%
41399
 
5.7%
33350
 
5.0%
22204
 
2.9%
23152
 
2.2%
31131
 
1.9%
Other values (3)186
 
2.7%

Length

2025-12-07T22:35:11.603973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
211737
24.8%
111640
23.4%
13916
13.1%
12840
12.0%
32457
 
6.5%
41399
 
5.7%
33350
 
5.0%
22204
 
2.9%
23152
 
2.2%
31131
 
1.9%
Other values (3)186
 
2.7%

Most occurring characters

ValueCountFrequency (%)
17303
52.1%
23715
26.5%
32418
 
17.2%
4582
 
4.2%
N3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)14021
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
17303
52.1%
23715
26.5%
32418
 
17.2%
4582
 
4.2%
N3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14021
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
17303
52.1%
23715
26.5%
32418
 
17.2%
4582
 
4.2%
N3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14021
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
17303
52.1%
23715
26.5%
32418
 
17.2%
4582
 
4.2%
N3
 
< 0.1%

LAT
Real number (ℝ)

High correlation 

Distinct6867
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.144172
Minimum-14.322636
Maximum71.324702
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size54.9 KiB
2025-12-07T22:35:11.639757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-14.322636
5-th percentile26.241219
Q133.818447
median38.546171
Q341.203423
95-th percentile44.900363
Maximum71.324702
Range85.647338
Interquartile range (IQR)7.3849767

Descriptive statistics

Standard deviation5.9794824
Coefficient of variation (CV)0.16098037
Kurtosis2.5011721
Mean37.144172
Median Absolute Deviation (MAD)3.559973
Skewness-0.95493157
Sum260454.93
Variance35.75421
MonotonicityNot monotonic
2025-12-07T22:35:11.676326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.803214
 
0.1%
26.0058954
 
0.1%
33.5657583
 
< 0.1%
32.7708163
 
< 0.1%
37.7624733
 
< 0.1%
42.4192793
 
< 0.1%
28.5732753
 
< 0.1%
32.7259553
 
< 0.1%
33.9832062
 
< 0.1%
41.8854522
 
< 0.1%
Other values (6857)6982
99.6%
ValueCountFrequency (%)
-14.3226361
< 0.1%
6.9097591
< 0.1%
7.1026881
< 0.1%
7.3431771
< 0.1%
13.4330471
< 0.1%
13.4406491
< 0.1%
13.4650461
< 0.1%
15.1522471
< 0.1%
17.7161151
< 0.1%
17.966351
< 0.1%
ValueCountFrequency (%)
71.3247021
< 0.1%
64.857561
< 0.1%
64.8575161
< 0.1%
61.5985531
< 0.1%
61.1909681
< 0.1%
61.1901631
< 0.1%
61.1814431
< 0.1%
60.4904591
< 0.1%
60.1106261
< 0.1%
58.3848461
< 0.1%

LON
Real number (ℝ)

High correlation 

Distinct6867
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-90.358006
Minimum-170.74277
Maximum171.37813
Zeros0
Zeros (%)0.0%
Negative7005
Negative (%)99.9%
Memory size54.9 KiB
2025-12-07T22:35:11.712552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-170.74277
5-th percentile-121.5184
Q1-97.458857
median-86.138814
Q3-78.823886
95-th percentile-71.437511
Maximum171.37813
Range342.1209
Interquartile range (IQR)18.634971

Descriptive statistics

Standard deviation17.887259
Coefficient of variation (CV)-0.19795987
Kurtosis31.890932
Mean-90.358006
Median Absolute Deviation (MAD)9.4222525
Skewness1.6851804
Sum-633590.34
Variance319.95403
MonotonicityNot monotonic
2025-12-07T22:35:11.749446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-83.9086844
 
0.1%
-80.3151894
 
0.1%
-112.1046013
 
< 0.1%
-117.1920733
 
< 0.1%
-92.1165043
 
< 0.1%
-83.4309583
 
< 0.1%
-81.1965033
 
< 0.1%
-96.6821663
 
< 0.1%
-84.1708322
 
< 0.1%
-87.626712
 
< 0.1%
Other values (6857)6982
99.6%
ValueCountFrequency (%)
-170.7427741
< 0.1%
-159.3958831
< 0.1%
-158.081611
< 0.1%
-158.0564031
< 0.1%
-157.9837711
< 0.1%
-157.9265861
< 0.1%
-157.8883791
< 0.1%
-157.8738152
< 0.1%
-157.871
< 0.1%
-157.8691721
< 0.1%
ValueCountFrequency (%)
171.3781291
< 0.1%
158.1581891
< 0.1%
145.7217331
< 0.1%
144.8358211
< 0.1%
144.8089441
< 0.1%
144.8039781
< 0.1%
134.4737441
< 0.1%
-64.798561
< 0.1%
-64.9728661
< 0.1%
-65.6480521
< 0.1%

CBSA
Text

Distinct790
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Memory size368.7 KiB
2025-12-07T22:35:11.857700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.8248716
Min length1

Characters and Unicode

Total characters33832
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique246 ?
Unique (%)3.5%

Sample

1st row26620
2nd row13820
3rd row33860
4th row26620
5th row33860
ValueCountFrequency (%)
35620401
 
5.7%
n307
 
4.4%
31080280
 
4.0%
16980194
 
2.8%
33100153
 
2.2%
37980143
 
2.0%
19100110
 
1.6%
41980110
 
1.6%
14460102
 
1.5%
1206095
 
1.4%
Other values (780)5117
73.0%
2025-12-07T22:35:12.005522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
09284
27.4%
44005
11.8%
13704
 
10.9%
33473
 
10.3%
23239
 
9.6%
62746
 
8.1%
82532
 
7.5%
91830
 
5.4%
71363
 
4.0%
51349
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)33832
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
09284
27.4%
44005
11.8%
13704
 
10.9%
33473
 
10.3%
23239
 
9.6%
62746
 
8.1%
82532
 
7.5%
91830
 
5.4%
71363
 
4.0%
51349
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)33832
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
09284
27.4%
44005
11.8%
13704
 
10.9%
33473
 
10.3%
23239
 
9.6%
62746
 
8.1%
82532
 
7.5%
91830
 
5.4%
71363
 
4.0%
51349
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)33832
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
09284
27.4%
44005
11.8%
13704
 
10.9%
33473
 
10.3%
23239
 
9.6%
62746
 
8.1%
82532
 
7.5%
91830
 
5.4%
71363
 
4.0%
51349
 
4.0%

NMCBSA
Text

Distinct790
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Memory size503.3 KiB
2025-12-07T22:35:12.087414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length46
Median length36
Mean length23.728323
Min length1

Characters and Unicode

Total characters166383
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique246 ?
Unique (%)3.5%

Sample

1st rowHuntsville, AL
2nd rowBirmingham-Hoover, AL
3rd rowMontgomery, AL
4th rowHuntsville, AL
5th rowMontgomery, AL
ValueCountFrequency (%)
ca750
 
4.2%
city592
 
3.3%
new468
 
2.6%
tx441
 
2.5%
fl413
 
2.3%
ny-nj-pa401
 
2.2%
york-newark-jersey401
 
2.2%
san366
 
2.0%
n307
 
1.7%
los281
 
1.6%
Other values (920)13559
75.4%
2025-12-07T22:35:12.205851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a11803
 
7.1%
e11292
 
6.8%
10967
 
6.6%
-10298
 
6.2%
n9294
 
5.6%
o8919
 
5.4%
r7909
 
4.8%
i7258
 
4.4%
,6705
 
4.0%
l6690
 
4.0%
Other values (52)75248
45.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)166383
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a11803
 
7.1%
e11292
 
6.8%
10967
 
6.6%
-10298
 
6.2%
n9294
 
5.6%
o8919
 
5.4%
r7909
 
4.8%
i7258
 
4.4%
,6705
 
4.0%
l6690
 
4.0%
Other values (52)75248
45.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)166383
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a11803
 
7.1%
e11292
 
6.8%
10967
 
6.6%
-10298
 
6.2%
n9294
 
5.6%
o8919
 
5.4%
r7909
 
4.8%
i7258
 
4.4%
,6705
 
4.0%
l6690
 
4.0%
Other values (52)75248
45.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)166383
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a11803
 
7.1%
e11292
 
6.8%
10967
 
6.6%
-10298
 
6.2%
n9294
 
5.6%
o8919
 
5.4%
r7909
 
4.8%
i7258
 
4.4%
,6705
 
4.0%
l6690
 
4.0%
Other values (52)75248
45.2%

CBSATYPE
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size342.5 KiB
1
5993 
2
712 
0
 
307

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7012
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
15993
85.5%
2712
 
10.2%
0307
 
4.4%

Length

2025-12-07T22:35:12.239983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T22:35:12.260060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
15993
85.5%
2712
 
10.2%
0307
 
4.4%

Most occurring characters

ValueCountFrequency (%)
15993
85.5%
2712
 
10.2%
0307
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)7012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15993
85.5%
2712
 
10.2%
0307
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15993
85.5%
2712
 
10.2%
0307
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15993
85.5%
2712
 
10.2%
0307
 
4.4%

CSA
Text

Distinct173
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size353.1 KiB
2025-12-07T22:35:12.365814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.543069
Min length1

Characters and Unicode

Total characters17832
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.1%

Sample

1st row290
2nd row142
3rd row388
4th row290
5th row388
ValueCountFrequency (%)
n1602
22.8%
408476
 
6.8%
348370
 
5.3%
176199
 
2.8%
148185
 
2.6%
428168
 
2.4%
488166
 
2.4%
370161
 
2.3%
548157
 
2.2%
490125
 
1.8%
Other values (163)3403
48.5%
2025-12-07T22:35:12.517923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
43262
18.3%
82640
14.8%
22103
11.8%
01939
10.9%
N1602
9.0%
31522
8.5%
11405
7.9%
61157
 
6.5%
7904
 
5.1%
5801
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)17832
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
43262
18.3%
82640
14.8%
22103
11.8%
01939
10.9%
N1602
9.0%
31522
8.5%
11405
7.9%
61157
 
6.5%
7904
 
5.1%
5801
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)17832
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
43262
18.3%
82640
14.8%
22103
11.8%
01939
10.9%
N1602
9.0%
31522
8.5%
11405
7.9%
61157
 
6.5%
7904
 
5.1%
5801
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)17832
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
43262
18.3%
82640
14.8%
22103
11.8%
01939
10.9%
N1602
9.0%
31522
8.5%
11405
7.9%
61157
 
6.5%
7904
 
5.1%
5801
 
4.5%

NMCSA
Text

Distinct173
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size504.4 KiB
2025-12-07T22:35:12.595515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length60
Median length44
Mean length23.883058
Min length1

Characters and Unicode

Total characters167468
Distinct characters61
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.1%

Sample

1st rowHuntsville-Decatur, AL
2nd rowBirmingham-Hoover-Talladega, AL
3rd rowMontgomery-Selma-Alexander City, AL
4th rowHuntsville-Decatur, AL
5th rowMontgomery-Selma-Alexander City, AL
ValueCountFrequency (%)
n1602
 
9.5%
ca611
 
3.6%
new515
 
3.0%
york-newark476
 
2.8%
ny-nj-ct-pa476
 
2.8%
los370
 
2.2%
angeles-long370
 
2.2%
beach370
 
2.2%
san344
 
2.0%
fl290
 
1.7%
Other values (324)11484
67.9%
2025-12-07T22:35:12.720587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
-13732
 
8.2%
a11421
 
6.8%
e11170
 
6.7%
9896
 
5.9%
n9571
 
5.7%
o9153
 
5.5%
r7854
 
4.7%
l6469
 
3.9%
i6341
 
3.8%
t6084
 
3.6%
Other values (51)75777
45.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)167468
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
-13732
 
8.2%
a11421
 
6.8%
e11170
 
6.7%
9896
 
5.9%
n9571
 
5.7%
o9153
 
5.5%
r7854
 
4.7%
l6469
 
3.9%
i6341
 
3.8%
t6084
 
3.6%
Other values (51)75777
45.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)167468
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
-13732
 
8.2%
a11421
 
6.8%
e11170
 
6.7%
9896
 
5.9%
n9571
 
5.7%
o9153
 
5.5%
r7854
 
4.7%
l6469
 
3.9%
i6341
 
3.8%
t6084
 
3.6%
Other values (51)75777
45.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)167468
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
-13732
 
8.2%
a11421
 
6.8%
e11170
 
6.7%
9896
 
5.9%
n9571
 
5.7%
o9153
 
5.5%
r7854
 
4.7%
l6469
 
3.9%
i6341
 
3.8%
t6084
 
3.6%
Other values (51)75777
45.2%

NECTA
Categorical

High correlation  Imbalance 

Distinct38
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size343.8 KiB
N
6680 
71650
 
109
73450
 
27
77200
 
27
78100
 
23
Other values (33)
 
146

Length

Max length5
Median length1
Mean length1.1893896
Min length1

Characters and Unicode

Total characters8340
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.1%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N6680
95.3%
71650109
 
1.6%
7345027
 
0.4%
7720027
 
0.4%
7810023
 
0.3%
7960018
 
0.3%
7570014
 
0.2%
7195012
 
0.2%
7675010
 
0.1%
787009
 
0.1%
Other values (28)83
 
1.2%

Length

2025-12-07T22:35:12.758622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n6680
95.3%
71650109
 
1.6%
7345027
 
0.4%
7720027
 
0.4%
7810023
 
0.3%
7960018
 
0.3%
7570014
 
0.2%
7195012
 
0.2%
7675010
 
0.1%
787009
 
0.1%
Other values (28)83
 
1.2%

Most occurring characters

ValueCountFrequency (%)
N6680
80.1%
0472
 
5.7%
7407
 
4.9%
5234
 
2.8%
6159
 
1.9%
1154
 
1.8%
460
 
0.7%
947
 
0.6%
344
 
0.5%
242
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)8340
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N6680
80.1%
0472
 
5.7%
7407
 
4.9%
5234
 
2.8%
6159
 
1.9%
1154
 
1.8%
460
 
0.7%
947
 
0.6%
344
 
0.5%
242
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8340
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N6680
80.1%
0472
 
5.7%
7407
 
4.9%
5234
 
2.8%
6159
 
1.9%
1154
 
1.8%
460
 
0.7%
947
 
0.6%
344
 
0.5%
242
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8340
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N6680
80.1%
0472
 
5.7%
7407
 
4.9%
5234
 
2.8%
6159
 
1.9%
1154
 
1.8%
460
 
0.7%
947
 
0.6%
344
 
0.5%
242
 
0.5%

NMNECTA
Categorical

High correlation  Imbalance 

Distinct38
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size350.0 KiB
N
6680 
Boston-Cambridge-Newton, MA-NH
 
109
Hartford-East Hartford-Middletown, CT
 
27
Providence-Warwick, RI-MA
 
27
Springfield, MA-CT
 
23
Other values (33)
 
146

Length

Max length37
Median length1
Mean length2.0922704
Min length1

Characters and Unicode

Total characters14671
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.1%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N6680
95.3%
Boston-Cambridge-Newton, MA-NH109
 
1.6%
Hartford-East Hartford-Middletown, CT27
 
0.4%
Providence-Warwick, RI-MA27
 
0.4%
Springfield, MA-CT23
 
0.3%
Worcester, MA-CT18
 
0.3%
New Haven, CT14
 
0.2%
Bridgeport-Stamford-Norwalk, CT12
 
0.2%
Portland-South Portland, ME10
 
0.1%
Waterbury, CT9
 
0.1%
Other values (28)83
 
1.2%

Length

2025-12-07T22:35:12.797563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n6680
90.1%
ma-nh109
 
1.5%
boston-cambridge-newton109
 
1.5%
ct66
 
0.9%
ma-ct41
 
0.6%
hartford-east27
 
0.4%
hartford-middletown27
 
0.4%
providence-warwick27
 
0.4%
ri-ma27
 
0.4%
me26
 
0.4%
Other values (47)279
 
3.8%

Most occurring characters

ValueCountFrequency (%)
N6973
47.5%
o608
 
4.1%
-559
 
3.8%
r513
 
3.5%
e506
 
3.4%
t468
 
3.2%
n434
 
3.0%
406
 
2.8%
d340
 
2.3%
a336
 
2.3%
Other values (33)3528
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)14671
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N6973
47.5%
o608
 
4.1%
-559
 
3.8%
r513
 
3.5%
e506
 
3.4%
t468
 
3.2%
n434
 
3.0%
406
 
2.8%
d340
 
2.3%
a336
 
2.3%
Other values (33)3528
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14671
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N6973
47.5%
o608
 
4.1%
-559
 
3.8%
r513
 
3.5%
e506
 
3.4%
t468
 
3.2%
n434
 
3.0%
406
 
2.8%
d340
 
2.3%
a336
 
2.3%
Other values (33)3528
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14671
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N6973
47.5%
o608
 
4.1%
-559
 
3.8%
r513
 
3.5%
e506
 
3.4%
t468
 
3.2%
n434
 
3.0%
406
 
2.8%
d340
 
2.3%
a336
 
2.3%
Other values (33)3528
24.0%

CD
Text

Distinct442
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Memory size363.0 KiB
2025-12-07T22:35:12.920006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.9987165
Min length1

Characters and Unicode

Total characters28039
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0105
2nd row0107
3rd row0102
4th row0105
5th row0107
ValueCountFrequency (%)
7298175
 
2.5%
361054
 
0.8%
361251
 
0.7%
170740
 
0.6%
300034
 
0.5%
470533
 
0.5%
062733
 
0.5%
450633
 
0.5%
061333
 
0.5%
361732
 
0.5%
Other values (432)6494
92.6%
2025-12-07T22:35:13.083461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
06478
23.1%
13972
14.2%
23879
13.8%
32861
10.2%
42784
9.9%
62107
 
7.5%
51747
 
6.2%
71544
 
5.5%
81405
 
5.0%
91259
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)28039
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
06478
23.1%
13972
14.2%
23879
13.8%
32861
10.2%
42784
9.9%
62107
 
7.5%
51747
 
6.2%
71544
 
5.5%
81405
 
5.0%
91259
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)28039
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
06478
23.1%
13972
14.2%
23879
13.8%
32861
10.2%
42784
9.9%
62107
 
7.5%
51747
 
6.2%
71544
 
5.5%
81405
 
5.0%
91259
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)28039
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
06478
23.1%
13972
14.2%
23879
13.8%
32861
10.2%
42784
9.9%
62107
 
7.5%
51747
 
6.2%
71544
 
5.5%
81405
 
5.0%
91259
 
4.5%

SLDL
Text

Distinct2779
Distinct (%)39.6%
Missing0
Missing (%)0.0%
Memory size369.6 KiB
2025-12-07T22:35:13.200240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.9537935
Min length1

Characters and Unicode

Total characters34736
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1217 ?
Unique (%)17.4%

Sample

1st row01019
2nd row01055
3rd row01074
4th row01006
5th row01077
ValueCountFrequency (%)
n81
 
1.2%
3607532
 
0.5%
0604128
 
0.4%
0605326
 
0.4%
1700522
 
0.3%
0607822
 
0.3%
0601520
 
0.3%
3403019
 
0.3%
7200718
 
0.3%
0607518
 
0.3%
Other values (2769)6726
95.9%
2025-12-07T22:35:13.356159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
08794
25.3%
14403
12.7%
23845
11.1%
43359
 
9.7%
33141
 
9.0%
62783
 
8.0%
52332
 
6.7%
72157
 
6.2%
81867
 
5.4%
91786
 
5.1%
Other values (11)269
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)34736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
08794
25.3%
14403
12.7%
23845
11.1%
43359
 
9.7%
33141
 
9.0%
62783
 
8.0%
52332
 
6.7%
72157
 
6.2%
81867
 
5.4%
91786
 
5.1%
Other values (11)269
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)34736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
08794
25.3%
14403
12.7%
23845
11.1%
43359
 
9.7%
33141
 
9.0%
62783
 
8.0%
52332
 
6.7%
72157
 
6.2%
81867
 
5.4%
91786
 
5.1%
Other values (11)269
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)34736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
08794
25.3%
14403
12.7%
23845
11.1%
43359
 
9.7%
33141
 
9.0%
62783
 
8.0%
52332
 
6.7%
72157
 
6.2%
81867
 
5.4%
91786
 
5.1%
Other values (11)269
 
0.8%

SLDU
Text

Distinct1592
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Memory size369.9 KiB
2025-12-07T22:35:13.472610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.9942955
Min length1

Characters and Unicode

Total characters35020
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique387 ?
Unique (%)5.5%

Sample

1st row01007
2nd row01018
3rd row01025
4th row01002
5th row01026
ValueCountFrequency (%)
3602743
 
0.6%
0602542
 
0.6%
0603937
 
0.5%
7200136
 
0.5%
0600933
 
0.5%
4802633
 
0.5%
1700332
 
0.5%
4801329
 
0.4%
0602427
 
0.4%
0603427
 
0.4%
Other values (1582)6673
95.2%
2025-12-07T22:35:13.624130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
010833
30.9%
24602
13.1%
14061
 
11.6%
33731
 
10.7%
43062
 
8.7%
62302
 
6.6%
51896
 
5.4%
71635
 
4.7%
81433
 
4.1%
91376
 
3.9%
Other values (20)89
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)35020
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
010833
30.9%
24602
13.1%
14061
 
11.6%
33731
 
10.7%
43062
 
8.7%
62302
 
6.6%
51896
 
5.4%
71635
 
4.7%
81433
 
4.1%
91376
 
3.9%
Other values (20)89
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)35020
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
010833
30.9%
24602
13.1%
14061
 
11.6%
33731
 
10.7%
43062
 
8.7%
62302
 
6.6%
51896
 
5.4%
71635
 
4.7%
81433
 
4.1%
91376
 
3.9%
Other values (20)89
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)35020
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
010833
30.9%
24602
13.1%
14061
 
11.6%
33731
 
10.7%
43062
 
8.7%
62302
 
6.6%
51896
 
5.4%
71635
 
4.7%
81433
 
4.1%
91376
 
3.9%
Other values (20)89
 
0.3%

SCHOOLYEAR
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size397.3 KiB
2020-2021
7012 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters63108
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-2021
2nd row2020-2021
3rd row2020-2021
4th row2020-2021
5th row2020-2021

Common Values

ValueCountFrequency (%)
2020-20217012
100.0%

Length

2025-12-07T22:35:13.662511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T22:35:13.687196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2020-20217012
100.0%

Most occurring characters

ValueCountFrequency (%)
228048
44.4%
021036
33.3%
-7012
 
11.1%
17012
 
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)63108
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
228048
44.4%
021036
33.3%
-7012
 
11.1%
17012
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)63108
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
228048
44.4%
021036
33.3%
-7012
 
11.1%
17012
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)63108
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
228048
44.4%
021036
33.3%
-7012
 
11.1%
17012
 
11.1%

Interactions

2025-12-07T22:35:09.078616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:08.164487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:08.356300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:08.533548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:08.727174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:08.904748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:09.110111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:08.197956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:08.383718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:08.567108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:08.755908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:08.932737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:09.138095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:08.227409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:08.409916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:08.598060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:08.782558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:08.957575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:09.169644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:08.261988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:08.446669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:08.632281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:08.815072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:08.986827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:09.199166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:08.293793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:08.475364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:08.667145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:08.848706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:09.016154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:09.231033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:08.325124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:08.502876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:08.695824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:08.875570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:09.048872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-07T22:35:13.704252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
CBSATYPELATLOCALELONNECTANMNECTAOBJECTIDUNITIDXY
CBSATYPE1.0000.1380.6550.1240.1250.1250.1390.0310.1240.138
LAT0.1381.0000.3710.1480.0090.009-0.116-0.1260.1481.000
LOCALE0.6550.3711.0000.3450.1340.1340.0930.0050.3450.371
LON0.1240.1480.3451.0000.0000.0000.0570.0521.0000.148
NECTA0.1250.0090.1340.0001.0001.0000.1330.0000.0000.009
NMNECTA0.1250.0090.1340.0001.0001.0000.1330.0000.0000.009
OBJECTID0.139-0.1160.0930.0570.1330.1331.0000.8390.057-0.116
UNITID0.031-0.1260.0050.0520.0000.0000.8391.0000.052-0.126
X0.1240.1480.3451.0000.0000.0000.0570.0521.0000.148
Y0.1381.0000.3710.1480.0090.009-0.116-0.1260.1481.000

Missing values

2025-12-07T22:35:09.291963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-07T22:35:09.358850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

XYOBJECTIDUNITIDNAMESTREETCITYSTATEZIPSTFIPCNTYNMCNTYLOCALELATLONCBSANMCBSACBSATYPECSANMCSANECTANMNECTACDSLDLSLDUSCHOOLYEAR
0-86.56850234.7833681100654Alabama A & M University4900 Meridian StreetNormalAL357620101089Madison County1234.783368-86.56850226620Huntsville, AL1290Huntsville-Decatur, ALNN010501019010072020-2021
1-86.79934533.5056972100663University of Alabama at BirminghamAdministration Bldg Suite 1070BirminghamAL35294-01100101073Jefferson County1233.505697-86.79934513820Birmingham-Hoover, AL1142Birmingham-Hoover-Talladega, ALNN010701055010182020-2021
2-86.17401032.3626093100690Amridge University1200 Taylor RdMontgomeryAL36117-35530101101Montgomery County1232.362609-86.17401033860Montgomery, AL1388Montgomery-Selma-Alexander City, ALNN010201074010252020-2021
3-86.64044934.7245574100706University of Alabama in Huntsville301 Sparkman DrHuntsvilleAL358990101089Madison County1234.724557-86.64044926620Huntsville, AL1290Huntsville-Decatur, ALNN010501006010022020-2021
4-86.29567732.3643175100724Alabama State University915 S Jackson StreetMontgomeryAL36104-02710101101Montgomery County1232.364317-86.29567733860Montgomery, AL1388Montgomery-Selma-Alexander City, ALNN010701077010262020-2021
5-87.52959433.2070156100733University of Alabama System Office500 University Blvd. EastTuscaloosaAL354010101125Tuscaloosa County1233.207015-87.52959446220Tuscaloosa, AL1NNNN010701063010212020-2021
6-87.54597833.2118757100751The University of Alabama739 University BlvdTuscaloosaAL35487-01000101125Tuscaloosa County1233.211875-87.54597846220Tuscaloosa, AL1NNNN010701063010212020-2021
7-85.94526632.9247808100760Central Alabama Community College1675 Cherokee RdAlexander CityAL350100101123Tallapoosa County3232.924780-85.94526610760Alexander City, AL2388Montgomery-Selma-Alexander City, ALNN010301081010272020-2021
8-86.96469834.8067939100812Athens State University300 N Beaty StAthensAL356110101083Limestone County3134.806793-86.96469826620Huntsville, AL1290Huntsville-Decatur, ALNN010501005010012020-2021
9-86.17754432.36736010100830Auburn University at Montgomery7440 East DriveMontgomeryAL36117-35960101101Montgomery County1232.367360-86.17754433860Montgomery, AL1388Montgomery-Selma-Alexander City, ALNN010201074010252020-2021
XYOBJECTIDUNITIDNAMESTREETCITYSTATEZIPSTFIPCNTYNMCNTYLOCALELATLONCBSANMCBSACBSATYPECSANMCSANECTANMNECTACDSLDLSLDUSCHOOLYEAR
7002-85.10556342.9824047003496274Calvin University - Handlon Campus1728 W. Bluewater HighwayIoniaMI48846-95712626067Ionia County3242.982404-85.10556324340Grand Rapids-Kentwood, MI1266Grand Rapids-Kentwood-Muskegon, MINN260326086260192020-2021
7003-112.00228043.4810007004496283Provo College-Idaho Falls Campus1592 East 17th StreetIdaho FallsID83404-35381616019Bonneville County1343.481000-112.00228026820Idaho Falls, ID1292Idaho Falls-Rexburg-Blackfoot, IDNN160216033160332020-2021
7004-85.24043635.0923117005496292Platt College-Miller-Motte College-Chattanooga 24180 South Creek RoadChattangoogaTN37406-10214747065Hamilton County1235.092311-85.24043616860Chattanooga, TN-GA1174Chattanooga-Cleveland-Dalton, TN-GANN470347028470102020-2021
7005-84.38855633.7542197006496317Digital Film Academy - Atlanta10 Park Place SouthAtlantaGA30303-29131313121Fulton County1133.754219-84.38855612060Atlanta-Sandy Springs-Alpharetta, GA1122Atlanta--Athens-Clarke County--Sandy Springs, GA-ALNN130513058130362020-2021
7006-116.29857243.5909107007496326Eagle Gate College-Boise Campus9300 West Overland RoadBoiseID83709-25051616001Ada County1243.590910-116.29857214260Boise City, ID1147Boise City-Mountain Home-Ontario, ID-ORNN160116018160182020-2021
7007-116.99990033.7460007008496335Coastline Beauty College - Hemet2627 West Florida Avenue Suite 100HemetCA92545-36610606065Riverside County2233.746000-116.99990040140Riverside-San Bernardino-Ontario, CA1348Los Angeles-Long Beach, CANN063606042060232020-2021
7008-82.59435438.4472337009496371Elite Welding Academy1910 County Road OneSouth PointOH45680-88493939087Lawrence County2238.447233-82.59435426580Huntington-Ashland, WV-KY-OH1170Charleston-Huntington-Ashland, WV-OH-KYNN390639093390172020-2021
7009-115.15815336.1172617010496380Medspa Academies - NIMA National Institute of Modern Aesthetics3993 Howard Hughes Parkway Suite 150Las VegasNV89169-67453232003Clark County1236.117261-115.15815329820Las Vegas-Henderson-Paradise, NV1332Las Vegas-Henderson, NVNN320132016320102020-2021
7010-82.56584628.0424507011496414TechSherpas 36510213 Wilsky BlvdTampaFL336251212057Hillsborough County2128.042450-82.56584645300Tampa-St. Petersburg-Clearwater, FL1NNNN121412062120182020-2021
7011-122.49472048.7911947012496423Zorganics Institute Beauty and Wellness410 WEST BAKERVIEW ROAD SUITE 112BellinghamWA982265353073Whatcom County1348.791194-122.49472013380Bellingham, WA1NNNN530253042530422020-2021